Detailing the Regime Shifts in Maine Coastal Current Behavior

Published

April 8, 2025

Code 
{
  library(sf) 
  library(fvcom) 
  library(tidyverse)
  library(gmRi)
  library(patchwork)
  library(rnaturalearth)
  library(showtext)
  library(ncdf4)
  # Cyclic color palettes in scico
  # From: https://www.fabiocrameri.ch/colourmaps/
  library(scico)
  library(legendry)
}

# namespace conflicts
conflicted::conflict_prefer("select", "dplyr")
conflicted::conflict_prefer("filter", "dplyr")


# Set the theme
theme_set(
  theme_gmri() +
    theme(strip.text.y = element_text(angle = 0),
          legend.position = "bottom", 
          legend.title.position = "top", 
          legend.title = element_text(hjust = 0.5), 
          strip.text = element_text(size = 8),
          axis.text = element_text(size = 8)))

# Project paths
lob_ecol_path <- cs_path("mills", "Projects/Lobster ECOL")
fvcom_path    <- cs_path("res", "FVCOM/Lobster-ECOL")
poly_paths    <- cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")

# Shapefiles
new_england <- ne_states("united states of america", returnclass = "sf") %>% 
  filter(postal %in% c("VT", "ME", "RI", "MA", "CT", "NH", "NY", "MD", "VA", "NJ", "DE", "NC", "PA", "WV"))
canada <- ne_states("canada", returnclass = "sf")

# # # Support functions for FVCOM
source(here::here("R/FVCOM_Support.R"))


# Factor Levels
areas_northsouth <- c(
  "Eastern Maine",  
  "Western Maine",  
  "Central Maine",  
  "Eastern Mass",
  "W South Channel",
  "Nantucket Shoals",  
  "Off No Mans Land",  
  "Southern Mass",  
  "Rhode Island Shore",
  "Long Island Sound", 
  "Cholera Bank",
  "Off Long Island",   
  "New Jersey Shore",  
  "Barnegat Ridge",  
  "Five Fathom Bank",  
  "Virginia Shore",
  "GOM_GBK",         
  "SNE")
Code 
# Read Regions in
proj_path <- cs_path("mills", "Projects/Lobster ECOL")

# Load Shapefiles for inshore/offshore
poly_paths <- cs_path("mills", "Projects/Lobster ECOL/Spatial_Defs")

# Load inshore areas
inshore_areas <- map(
  setNames(
    list.files(str_c(poly_paths, "inshore_areas"), full.names = T),
    str_remove(list.files(str_c(poly_paths, "inshore_areas")), ".geojson")),
  function(x){read_sf(x)}
) %>% 
  bind_rows()

offshore_areas <- map(
  setNames(
    list.files(str_c(poly_paths, "offshore_areas"), full.names = T),
    str_remove(list.files(str_c(poly_paths, "offshore_areas")), ".geojson")),
  function(x){read_sf(x)}
) %>% bind_rows() %>% 
  mutate(SHORT_NAME = Region) 

# st_crs(offshore_areas)
# st_crs(inshore_areas)

# Combine so we can plot them
study_regions <- bind_rows(
  st_transform(inshore_areas, crs = st_crs(4269)),
  offshore_areas)
Code 
# Use GMRI style
use_gmri_style_rmd()

Reviewing STARS Regime Changes in Space/Time

This markdown reviews the various rstars regime shift results which were produced separately. I will begin at the largest geographic scales and work down to local timeseries:

Regime shifts for individual timeseries were tested using the STARS methodology. Any daily timeseries (temperature and salinity from ocean reanalysis models) were aggregated to a monthly temporal resolution, and any trends and seasonal cycles were removed.

The Marriott, Pope and Kendall (MPK) “pre-whitening” routine was used within the {rstars} algorithm to remove “red noise” (autoregressive processes, typically AR1) from the timeseries.

For more details on trend removal and pre-whitening methods see Rodionov 2006.

Shelf-Scale Shifts

There are 2-3 climate and oceanographic timeseries that operate at the broad regional scale of the Northeast shelf. These are:

  1. The Gulf Stream Index (a metric indicating the North/South position of the Gulf Stream) based on SSH
  2. The Northeast Channel Slopewater Proportions (the percentage of various water masses at the 150-200m depth entering GOM, using NERACOOS buoy data)
  3. The North Atlantic Oscillation (atmospheric pressure differential between icelandic low and the Azores High)

These three indices affect conditions over large spatial scales and and are likely to either directly or indirectly impact more local scale changes.

These three metrics are available in the ecodata r package and can be pulled directly from the package.

Code 
library(ecodata)

# GSI
# Why is there more than one value per month?
gsi <- ecodata::gsi %>% #glimpse()
  mutate(Time = as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))


# use the old one
gsi_old <- ecodata::gsi_old %>% #glimpse()
  mutate(Var = "gulf stream index old") %>% 
  mutate(Time = as.Date(str_c(str_replace(Time, "[.]", "-"), "-01")))


# NAO
nao <- ecodata::nao%>% 
  mutate(Time = as.Date(str_c(Time, "-01-01")))

# Put them together to plot
shelf_indices <- bind_rows(list(gsi, gsi_old, nao))

The Gulf Stream indices come as two monthly datasets, the other indices are annual. Any long-term trends for each metric have been removed prior to regime shift tests on these metrics.

Code 
# Run the shift test for summertime PCA
shelf_indices_detrended <- shelf_indices %>%
  split(.$Var) %>%
  map_dfr(function(.x){

    # Detrend
    .x <- .x %>% 
      arrange(Time) %>% 
      mutate(
        time = Time,
        yr_num = year(time))
    
    # annual trend
    trend_mod <- lm(Value ~ Time, data = .x)
    
    # save the results
    .x <- broom::augment(x = trend_mod) %>%
      rename(
        trend_fit = .fitted,
        trend_resid = .resid) %>% 
      full_join(.x, join_by(Time, Value)) %>% 
      mutate(trend_resid = if_else(is.na(Value), NA, trend_resid))
    
    return(.x)})



ggplot(shelf_indices_detrended, aes(Time, trend_resid, color = Var)) + 
  geom_line(linewidth = 0.6, alpha = 0.8) + 
  facet_grid(EPU * Var ~ ., scales = "free", labeller = label_wrap_gen(width= 8)) +
  guides(color = guide_legend(nrow = 2)) +
  labs(title = "Shelf Scale Ocean/Climate Metrics - Detrended")

Bacause the slopewater proportion contains NA values, we cannot evaluate it for breaks unless we impute missing values somehow.

Code 
# Stirnimann used these values in their paper:
# l = 5, 10, 15, 17.5 years, with monthly data
# Huber = 1
# Subsampling = (l + 1) / 3


# Load the function(s)
source(here::here("rstars-master","rSTARS.R"))
Code 
shelf_indices_detrended %>%
  filter(Var == "proportion ne") %>%
  glimpse()
Rows: 0
Columns: 14
$ Value       <dbl> 
$ Time        <date> 
$ trend_fit   <dbl> 
$ trend_resid <dbl> 
$ .hat        <dbl> 
$ .sigma      <dbl> 
$ .cooksd     <dbl> 
$ .std.resid  <dbl> 
$ X           <int> 
$ Var         <chr> 
$ EPU         <chr> 
$ Units       <chr> 
$ time        <date> 
$ yr_num      <dbl>
Code 
# Run the regime shift test
shelf_indices_rstars <- shelf_indices_detrended %>%
  split(.$Var) %>%
  map_dfr(function(.x){
    
    cutoff_length <- ifelse(
      str_detect(.x$Var[[1]], "index"),
      12*7,
      7)
    
    # This is only here because we have duplicate dates in the GSI
    .x <- distinct(.x, Time, .keep_all = T)
    
    # Get the results from that
    x_rstars <- rstars(
      data.timeseries = as.data.frame(
        .x[,c("Time", "trend_resid")]),
      l.cutoff = cutoff_length,
      pValue = 0.05,
      Huber = 1,
      Endfunction = T,
      preWhitening = T,
      OLS = F,
      MPK = T,
      IP4 = F,
      SubsampleSize = (cutoff_length + 1)/3,
      returnResults = T) %>% 
      mutate(
        EPU = .x$EPU[[1]],
        Value = .x$Value,
        shift_direction = case_when(
          RSI > 0 ~ "Shift Up", 
          RSI < 0 ~ "Shift Down",
          TRUE ~ NA))
    
    },
    
    .id = "Var"
  )

The results can be seen below:

Code 
# Summarise the breakpoint locations
shelf_shift_points <- shelf_indices_rstars %>% 
  filter(RSI != 0) %>% 
  dplyr::select(Time, Var, EPU, shift_direction)


# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = shelf_shift_points,
    aes(
      xintercept = Time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = shelf_indices_rstars,
    aes(Time, Value),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(EPU * Var~., labeller = label_wrap_gen(width = 8)) +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "Shelf Scale Ocean/Climate Metrics - Detrended",
       subtitle = "STARS Regime Shifts")

Based on these results, there is some evidence for breakpoints in the Gulf Stream Indices, and not in the NAO index.

Code 
shelf_shift_points %>% 
  group_by(Var) %>% 
  arrange(Time, EPU) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "Shelf-Scale Breaks")
Shelf-Scale Breaks
Time EPU shift_direction
gulf stream index
1957-12-01 All Shift Down
1971-09-01 All Shift Up
1977-08-01 All Shift Down
2003-01-01 All Shift Down
2012-02-01 All Shift Up
western gulf stream index
1962-02-01 All Shift Down
1971-07-01 All Shift Up
2003-01-01 All Shift Down
2011-12-01 All Shift Up
gulf stream index old
2003-01-01 All Shift Down

EPU-Scale Shifts

For the EPU-scale metrics we have the cold-pool index, the Northeast Channel Slopewater Proportion (from NERACOOS Buoy N), as well as metrics of primary production and zooplankton community. We also have temperature and salinity timeseries specific to each area.

Temperature and salinity is from either GLORYS or FVCOM, primary productivity is satellite derived (OC-CCI, SeaWiFS, MODIS-Aqua), , and the zooplankton community indices are from the Gulf of Maine CPR transect.

Code 
# # There are a ton here. We want primary productivity / chlor a, and maybe anomalies
# chl_pp <- ecodata::chl_pp %>% 
#   filter(str_detect(Var, "MONTHLY")) %>% 
#   filter(str_detect(Var, "PPD|CHLOR_A")) %>% 
#   separate(col = "Time", into = c("Period", "Time"), sep = "_") %>% 
#   mutate(Time = as.Date(
#     str_c(
#       str_sub(Time, 1, 4),
#       str_sub(Time, 5, 6), 
#       "01",
#       sep = "-")))

# Annual will make life easier
annual_chl_pp <- ecodata::annual_chl_pp %>% 
  filter(str_detect(Var, "MEAN")) %>% 
  separate(col = "Time", into = c("Period", "Time"), sep = "_") %>% 
  mutate(Time = as.Date(
    str_c(
      Time,
      "01-01",
      sep = "-")))


# Just take one cold pool index for now
cold_pool <- ecodata::cold_pool %>% #distinct(Source)
  filter(Var == "cold_pool_index") %>% 
  mutate(
    Var = str_c(Source, Var, sep = "_"),
    Time = as.Date(
    str_c(
      Time,
      "01-01",
      sep = "-")))

# Slopewater
slopewater <- ecodata::slopewater %>% 
  mutate(Time = as.Date(str_c(Time, "-01-01")))  %>% 
  filter(Time > as.Date("1990-01-01")) %>% 
  drop_na()


# Combine those
epu_indices <- bind_rows(annual_chl_pp, slopewater, cold_pool) %>% 
  group_by(Var, EPU) %>% 
  arrange(Time) 


# Plot them
ggplot(epu_indices, aes(Time, Value, color = Var)) +
  geom_line(linewidth = 0.6, alpha = 0.8) +
  facet_grid(Var * EPU ~ ., scales = "free", labeller = label_wrap_gen(width= 8)) +
  guides(color = guide_legend(nrow = 3)) +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  guides(color = guide_legend(nrow = 2)) +
  labs(title = "EPU Scale Ocean/Ecological Metrics")

Code 
# Detrend the epu stuff

epu_indices_detrended <- epu_indices %>% 
  mutate(var_epu = str_c(Var, EPU, sep = "X")) %>% 
  split(.$var_epu) %>%
  map_dfr(function(.x){

    # Detrend
    .x <- .x %>% 
      arrange(Time) %>% 
      mutate(
        time = Time,
        yr_num = year(time))
    
    # annual trend
    trend_mod <- lm(Value ~ Time, data = .x)
    
    # save the results
    .x <- broom::augment(x = trend_mod) %>%
      rename(
        trend_fit = .fitted,
        trend_resid = .resid) %>% 
      full_join(.x, join_by(Time, Value)) %>% 
      mutate(trend_resid = if_else(is.na(Value), NA, trend_resid))
    
    return(.x)})


# Plot detrended
ggplot(epu_indices_detrended, aes(Time, trend_resid, color = Var)) + 
  geom_line(linewidth = 0.6, alpha = 0.8) + 
  facet_grid(EPU * Var ~ ., scales = "free", labeller = label_wrap_gen(width= 8)) +
  guides(color = guide_legend(nrow = 3)) +
    scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  labs(title = "EPU Scale Ocean/Climate Metrics - Detrended",
       y = "Metric")

Code 
# Run rstars for those, temperature and salinity are done


# Run the regime shift test
epu_indices_rstars <- epu_indices_detrended %>%
  #filter(str_detect(Var, "proportion") == FALSE) %>% 
  split(.$var_epu) %>%
  map_dfr(function(.x){
    
    # This is only here because we have duplicate dates in the GSI
    .x <- distinct(.x, Time, .keep_all = T)
    
    # cutoff length
    cutoff_length <- 7
    
    # Get the results from that
    x_rstars <- rstars(
      data.timeseries = as.data.frame(
        .x[,c("Time", "trend_resid")]),
      l.cutoff = cutoff_length,
      pValue = 0.05,
      Huber = 1,
      Endfunction = T,
      preWhitening = T,
      OLS = F,
      MPK = T,
      IP4 = F,
      SubsampleSize = (cutoff_length + 1)/3,
      returnResults = T) %>% 
      mutate(
        Value = .x$Value,
        shift_direction = case_when(
          RSI > 0 ~ "Shift Up", 
          RSI < 0 ~ "Shift Down",
          TRUE ~ NA))
    
    },
    
    .id = "var_epu"
  ) %>% 
  separate(var_epu, into = c("Var", "EPU"), sep = "X")

Primary Production and Cold-Pool Dynamics

The results can be seen below:

Code 
# Summarise the breakpoint locations
epu_shift_points <- epu_indices_rstars %>% 
  filter(RSI != 0) %>% 
  dplyr::select(Time, Var, EPU, shift_direction)


# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = epu_shift_points,
    aes(
      xintercept = Time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = epu_indices_rstars,
     aes(Time, Value),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(EPU * Var~., labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "EPU Scale Ocean/Climate Metrics - Detrended",
       subtitle = "STARS Regime Shifts")

Based on these results, we see no breakpoints in either primary production or the cold pool index.

Code 
epu_shift_points %>% 
  group_by(Var) %>% 
  arrange(Time, EPU) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "EPU Scale Breaks")
EPU Scale Breaks
Time EPU shift_direction

EPU Temperature and Salinity

Temperature and salinity changes were done separately, here are their results

Code 
# Load Temperature and Salinity, Pull out the EPU scale timeseries


# # Save them
surf_sal_monthly_shifts <- read_csv(here::here("rstars_results/lobecol_ssal_monthly_shifts_detrended.csv")) %>% 
  mutate(Var = "Surface Salinity") 
bot_sal_monthly_shifts <- read_csv(here::here("rstars_results/lobecol_bsal_monthly_shifts_detrended.csv"))  %>% 
  mutate(Var = "Bottom Salinity")
surf_temp_monthly_shifts <- read_csv(here::here("rstars_results/lobecol_stemp_monthly_shifts_detrended.csv"))  %>% 
  mutate(Var = "Surface Temperature") 
bot_temp_monthly_shifts <- read_csv(here::here("rstars_results/lobecol_btemp_monthly_shifts_detrended.csv"))  %>% 
  mutate(Var = "Bottom Temperature") 

# Put them together
tempsal <- bind_rows(list(surf_sal_monthly_shifts, bot_sal_monthly_shifts, surf_temp_monthly_shifts, bot_temp_monthly_shifts)) %>% 
  mutate(shift_direction = case_when(
          RSI > 0 ~ "Shift Up", 
          RSI < 0 ~ "Shift Down",
          TRUE ~ NA),
         area_id = factor(area_id, levels = areas_northsouth))
offshore_tempsal <- tempsal %>% filter(area_id %in% c("GOM_GBK", "SNE"))
inshore_tempsal <- tempsal %>% filter(area_id %in% c("GOM_GBK", "SNE") == FALSE)


# Pull the shift points
offshore_tempsal_shifts <- offshore_tempsal %>% 
  filter(RSI != 0) %>% 
  dplyr::select(time, Var, area_id, shift_direction)
inshore_tempsal_shifts <- inshore_tempsal %>% 
  filter(RSI != 0) %>% 
  dplyr::select(time, Var, area_id, shift_direction)
Code 
# Plot the offshore shifts


# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = offshore_tempsal_shifts,
    aes(
      xintercept = time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = offshore_tempsal,
     aes(time, model_resid),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(area_id * Var~., labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1978-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "FVCOM EPU Scale Temp/Sal Metrics",
       subtitle = "STARS Regime Shifts")

A change in SNE salinity appears to have occured around 1992.

Surface temperatures fell in SNE around 2002, but they rose again in 2011 along with GOM+GBK the same year.

Code 
offshore_tempsal_shifts %>% 
  group_by(Var) %>% 
  arrange(time, area_id) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "EPU Scale Temp+Sal Breaks")
EPU Scale Temp+Sal Breaks
time area_id shift_direction
Surface Salinity
1992-06-15 SNE Shift Down
Bottom Salinity
1992-07-15 SNE Shift Down
Surface Temperature
2002-11-15 SNE Shift Down
2011-05-15 SNE Shift Up
2011-08-15 GOM_GBK Shift Up

CPR Community PCA Index

Code 
# From Pershing & Kemberling
# PC1 explains 53.62% of variance
# PC2 explains 27.9%
# PC1 is associated with centropages, oithona, para-pseudocalanus
# PC2 is C. Fin, Metridia, & Euphausiacea

# Load the CPR data
cpr_community <- read_csv(here::here("local_data", "cpr_focal_pca_timeseries_period_1961-2017.csv")) %>% 
  rename(
    PC1_small_zoo = `First Mode`,
    PC2_large_zoo = `Second Mode`) %>% 
  select(-c(pca_period, taxa_used)) %>% 
  pivot_longer(cols = starts_with("PC"), names_to = "Var", values_to = "value")
Code 
# Run breakpoints in CPR PCA


# Run the regime shift test
cpr_indices_rstars <- cpr_community %>%
  filter(year > 1976) %>% 
  mutate(EPU = "GOM",
         var_epu = str_c(Var, "X", EPU)) %>% 
  split(.$var_epu) %>%
  map_dfr(function(.x){
    
    # detrend
    trend_mod <- lm(value ~ year, data = .x)
    .x$trend_resid <- resid(trend_mod)
    
    # cutoff length
    cutoff_length <- 7
    
    # Get the results from that
    x_rstars <- rstars(
      data.timeseries = as.data.frame(
        .x[,c("year", "trend_resid")]),
      l.cutoff = cutoff_length,
      pValue = 0.05,
      Huber = 1,
      Endfunction = T,
      preWhitening = T,
      OLS = F,
      MPK = T,
      IP4 = F,
      SubsampleSize = (cutoff_length + 1)/3,
      returnResults = T) %>% 
      mutate(
        Value = .x$value,
        shift_direction = case_when(
          RSI > 0 ~ "Shift Up", 
          RSI < 0 ~ "Shift Down",
          TRUE ~ NA))
    
    },
    
    .id = "var_epu"
  ) %>% 
  separate(var_epu, into = c("Var", "EPU"), sep = "X") %>% 
  mutate(time = as.Date(str_c(year, "-01-01")))
Code 
# Summarise the breakpoint locations
cpr_shift_points <- cpr_indices_rstars %>% 
  filter(RSI != 0) %>% 
  dplyr::select(time, Var, EPU, shift_direction)


# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = cpr_shift_points,
    aes(
      xintercept = time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = cpr_indices_rstars,
     aes(time, trend_resid),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(EPU ~ Var, labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "CPR Zooplankton Community Metrics",
       subtitle = "STARS Regime Shifts")

ECOMON Community PCA Index

Code 
# Abundance per 100m3 for different taxa
# ecodata::zoo_regime %>% distinct(Var) %>% pull() %>% sort()
ecomon_zoo <- ecodata::zoo_regime
ecomon_zoo %>% 
  filter(!str_detect(Var, "fish|clauso|gas")) %>% 
  ggplot(aes(Time, Value)) +
  geom_line() +
  facet_grid(Var ~ EPU)

Code 
# We might be able to just pull out the seven focal species and then repeat the PCA process...
# Issues:
# ecomon doesn't split the calanus into adult/juvenile
# Para and Pseaudocalana are split
# Euph also has a Euph1

Large and Small Copepod Index

https://noaa-edab.github.io/tech-doc/zoo_abundance_anom.html?q=zoopl#copepod

Abundance anomalies are computed from the expected abundance on the day of sample collection. Abundance anomaly time series are constructed for Centropages typicus, Pseudocalanus spp., Calanus finmarchicus, and total zooplankton biovolume. The small-large copepod size index is computed by averaging the individual abundance anomalies of Pseudocalanus spp., Centropages hamatus, Centropages typicus, and Temora longicornis, and subtracting the abundance anomaly of Calanus finmarchicus. This index tracks the overall dominance of the small bodied copepods relative to the largest copepod in the Northeast U.S. region, Calanus finmarchicus.

Code 
# This has "LgCopepods" & "SmCopepods", which could produce large/small index
# ecodata::zoo_abundance_anom %>% distinct(Var) %>% pull() %>% sort()


zoo_lg_small <- ecodata::zoo_abundance_anom %>% 
  filter(Var %in% c("LgCopepods", "SmCopepods")) %>%
  mutate(Value = as.numeric(Value)) %>% 
  pivot_wider(values_from = "Value", names_from = "Var") %>% 
  mutate(small_large_index =  SmCopepods - LgCopepods)



ggplot(zoo_lg_small, aes(Time, small_large_index)) +
  geom_line() +
  geom_hline(yintercept = 0) +
  facet_grid(EPU~., scales = "free") +
  labs(
    y = "Small-Large Copepod Index\n(More Large Copepods  <----->  More Small Copepods)")

Code 
# # This is too many vars
# ecodata::zooplankton_index %>% distinct(Var) %>% pull()
# 
# # Not informative mechanistically
# ecodata::zoo_diversity
# 
# # BOLD move on absolute abundances
# ecodata::zoo_strat_abun

NEEDS: MCC & Lobster Predator Indices

There are two EPU-Scale indices that we need to develop. This is the MCC index, and a lobster predator abundance index.

The Gulf of Maine Coastal Current plays an important role in transporting lobster larva and their recruitment form year-to-year. The degree of “connected-ness” of the Western and Eastern portions of this current have been used in the past to inform expectations of lobster recruitment.

Local/Nearshore Shifts

Temperature and Salinity

Code 
# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = filter(inshore_tempsal_shifts, str_detect(Var, "Salinity")),
    aes(
      xintercept = time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = filter(inshore_tempsal, str_detect(Var, "Salinity")),
     aes(time, model_resid),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(area_id~Var, labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1978-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "FVCOM Inshore Scale Sal Metrics",
       subtitle = "STARS Regime Shifts")

Code 
ggplot() +
  geom_sf(data = filter(
    study_regions, 
    SHORT_NAME %in% 
      (dplyr::filter(inshore_tempsal_shifts, str_detect(Var, "Salinity")) %>% 
      pull(area_id))
    ),
    fill = gmri_cols("gmri blue"), alpha = 0.4) +
  geom_sf(data = new_england) +
  geom_sf(data = canada) +
  
  coord_sf(xlim = c(-78, -66), ylim = c(35.5, 45)) +
  labs(title = "Affected Areas")

Code 
# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = filter(inshore_tempsal_shifts, !str_detect(Var, "Salinity")),
    aes(
      xintercept = time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = filter(inshore_tempsal, !str_detect(Var, "Salinity")),
     aes(time, model_resid),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(area_id~Var, labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1978-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "FVCOM Inshore Scale Temperature Metrics",
       subtitle = "STARS Regime Shifts")

Code 
ggplot() +
  geom_sf(data = filter(
    study_regions, 
    SHORT_NAME %in% 
      (dplyr::filter(inshore_tempsal_shifts, !str_detect(Var, "Salinity")) %>% 
      pull(area_id))
    ),
    fill = gmri_cols("lv orange"), alpha = 0.4) +
  geom_sf(data = new_england) +
  geom_sf(data = canada) +
  
  coord_sf(xlim = c(-78, -66), ylim = c(35.5, 45)) +
  labs(title = "Affected Areas")

Code 
inshore_tempsal_shifts %>% 
  filter(str_detect(Var, "Salinity")) %>% 
  group_by(Var) %>% 
  arrange(time, area_id) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "Inshore Scale Temp+Sal Breaks")
Inshore Scale Temp+Sal Breaks
time area_id shift_direction
Bottom Salinity
1992-07-15 W South Channel Shift Down
1995-07-15 Barnegat Ridge Shift Down
1995-12-15 Virginia Shore Shift Down
Surface Salinity
1994-12-15 Eastern Maine Shift Down
1995-07-15 Barnegat Ridge Shift Down
1995-12-15 Virginia Shore Shift Down
2004-06-15 Cholera Bank Shift Down
2008-05-15 Cholera Bank Shift Up
Code 
inshore_tempsal_shifts %>% 
  filter(!str_detect(Var, "Salinity")) %>% 
  group_by(Var) %>% 
  arrange(time, area_id) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "Inshore Scale Temp+Sal Breaks")
Inshore Scale Temp+Sal Breaks
time area_id shift_direction
Surface Temperature
1984-03-15 Southern Mass Shift Down
1986-05-15 Western Maine Shift Down
1986-05-15 Central Maine Shift Down
1986-05-15 Long Island Sound Shift Down
1987-02-15 Eastern Maine Shift Down
2002-11-15 Eastern Mass Shift Down
2002-11-15 W South Channel Shift Down
2002-11-15 Cholera Bank Shift Down
2002-11-15 Off Long Island Shift Down
2002-11-15 New Jersey Shore Shift Down
2002-11-15 Barnegat Ridge Shift Down
2003-01-15 Western Maine Shift Down
2009-07-15 Barnegat Ridge Shift Up
2009-11-15 Eastern Maine Shift Up
2009-11-15 Western Maine Shift Up
2009-11-15 Central Maine Shift Up
2009-11-15 Eastern Mass Shift Up
2009-11-15 Southern Mass Shift Up
2009-11-15 New Jersey Shore Shift Up
2010-02-15 W South Channel Shift Up
2011-02-15 Long Island Sound Shift Up
2011-02-15 Five Fathom Bank Shift Up
2011-03-15 Off No Mans Land Shift Up
2011-05-15 Cholera Bank Shift Up
2011-05-15 Off Long Island Shift Up
Bottom Temperature
1984-03-15 Southern Mass Shift Down
1986-05-15 Long Island Sound Shift Down
2002-11-15 Off No Mans Land Shift Down
2002-11-15 Cholera Bank Shift Down
2002-11-15 New Jersey Shore Shift Down
2002-11-15 Barnegat Ridge Shift Down
2002-11-15 Virginia Shore Shift Down
2002-12-15 W South Channel Shift Down
2009-11-15 Southern Mass Shift Up
2010-03-15 Nantucket Shoals Shift Up
2010-09-15 W South Channel Shift Up
2011-02-15 Long Island Sound Shift Up
2011-02-15 Cholera Bank Shift Up
2011-02-15 New Jersey Shore Shift Up
2011-02-15 Barnegat Ridge Shift Up
2011-02-15 Virginia Shore Shift Up
2011-08-15 Off No Mans Land Shift Up

Days in Key Temperature Ranges

In addition to breaks in absolute temperatures, there is interest in the amount of time spent in favorable (12-18C) and unfavorable conditions (20C).

These use daily bottom temperatures:

Code 
daily_fvcom_temps <- read_csv(
  str_c(lob_ecol_path, "FVCOM_processed/area_timeseries/all_regions_fvcom_temperatures_daily.csv")) %>% 
  mutate(depth_type = if_else(area_id %in% c("GOM_GBK", "SNE"), "offshore", "nearshore"))


# The degday functions were built with the ability to model daily cycles
# the sine and triangle methods accomodate this
# since we only have daily data the simple average is probably the way to do it
thresh_low <- 10
thresh_up  <- 18

# library(degday)
# Get Monthly Totals in Ranges
dd_monthly <- daily_fvcom_temps %>%
  mutate(
    year = lubridate::year(time),
    month = lubridate::month(time),
    opt_btemp     = if_else(between(bottom_t, 10,18), 1, 0),
    stress_btemp  = if_else(bottom_t > 18, 1, NA),
    cold_btemp     = if_else(bottom_t < 10, 1, NA)) %>% 
  group_by(area_id, year, month) %>% 
  summarise(
    across(
      ends_with("temp"), 
      ~sum(.x, na.rm = T)),
    .groups = "drop") %>% 
  pivot_longer(
    cols = ends_with("temp"), 
    names_to = "var", 
    values_to = "totals") %>% 
  mutate(
    time = as.Date(str_c(year,"-01-01")) + months(month-1),
    area_id = factor(area_id, levels = areas_northsouth),
    var = case_match(
      var,
      "opt_btemp" ~ "Preferred Bottom Temperatures 10-18C", 
      "stress_btemp" ~ "Heat Stress Conditions >18C",
      "cold_btemp" ~ "Below Preferred Conditions <10C")
  )

# Plot them
dd_monthly %>%
  filter(area_id %in% c("GOM_GBK", "SNE")) %>%
  ggplot() +
  geom_point(
    aes(time, y = totals, color = var),
    linewidth = 0.6) +
  geom_smooth(
    aes(time, y = totals, color = var),
    method = "loess") +
  scale_color_manual(values = c("lightblue",  "#ea4f12", "#057872")) +
  facet_grid(area_id~.) +
  
  theme(strip.text.y = element_text(angle = 0),
      legend.position = "bottom") +
  guides(fill = guide_legend(
    nrow = 2,
    title.position = "top",
    title.hjust = 0.5))+
  labs(y = "Days in Range",
       fill = "Daily Temperature Conditions", color = "",
       title = "FVCOM Bottom Temperature Degree-Days")

Code 
# Do annual, Monthly values looked insane
dd_annual <- dd_monthly %>%
  group_by(year, area_id, var) %>% 
  summarise(across(totals, mean)) %>%
  mutate(var_area = str_c(var, area_id, sep = "X"),
         time = as.Date(str_c(year, "-01-01")))

# Plot them
dd_annual %>% 
  ggplot() +
  geom_area(
    aes(year, y = totals, fill = var)) +
  scale_fill_manual(values = c("lightblue",  "#ea4f12", "#057872")) +
  facet_grid(area_id~.) +
  
  theme(strip.text.y = element_text(angle = 0),
      legend.position = "bottom") +
  guides(fill = guide_legend(
    nrow = 2,
    title.position = "top",
    title.hjust = 0.5))+
  labs(y = "Days in Range",
       fill = "Daily Temperature Conditions", color = "",
       title = "FVCOM Bottom Temperature Degree-Days")

Code 
# # Remove monthly averages, and trends
dd_annual_detrended <-  dd_annual  %>%
  split(.$var_area) %>%
  map_dfr(function(.x){

    # Detrend
    .x <- .x %>%
      arrange(year) %>%
      mutate(yr_num = as.numeric(year))

    # annual trend
    # trend_mod <- lm(totals ~ yr_num, data = .x)

    # annual trend + monthly average
    trend_mod <- lm(totals ~ yr_num, data = .x)

    # save the results
    .x <- broom::augment(x = trend_mod) %>%
      rename(
        trend_fit = .fitted,
        trend_resid = .resid) %>%
      full_join(.x, join_by(yr_num, totals)) %>%
      mutate(trend_resid = if_else(is.na(totals), NA, trend_resid))

    return(.x)}) %>% 
  mutate(time = as.Date(str_c(year, "-01-01")))



# Plot
dd_annual_detrended %>% 
  filter(area_id %in% c("GOM_GBK", "SNE")) %>%
  ggplot(aes(time, trend_resid, color = var)) +
  geom_point() +
  geom_smooth(method = "loess", linewidth = 0.6) +
  scale_color_manual(values = c("lightblue",  "#ea4f12", "#057872")) +
  facet_grid(area_id ~ ., scales = "free", labeller = label_wrap_gen(width= 8)) +
  guides(color = guide_legend(nrow = 3)) +
  labs(title = "EPU Scale Ocean/Climate Metrics - Detrended")

Code 
# Run the regime shift test
temp_range_rstars <- dd_annual_detrended %>%
# temp_range_rstars <-   dd_annual %>% 
  split(.$var_area) %>%
  map_dfr(function(.x){
    
    # Seven years
    cutoff_length <- 7
    
    
    # Get the results from that
    x_rstars <- rstars(
      data.timeseries = as.data.frame(
        .x[,c("time", "trend_resid")]),
        # .x[,c("time", "totals")]),
      l.cutoff = cutoff_length,
      pValue = 0.05,
      Huber = 1,
      Endfunction = T,
      preWhitening = T,
      OLS = F,
      MPK = T,
      IP4 = F,
      SubsampleSize = (cutoff_length + 1)/3,
      returnResults = T) %>% 
      mutate(
        Value = .x$totals,
        shift_direction = case_when(
          RSI > 0 ~ "Shift Up", 
          RSI < 0 ~ "Shift Down",
          TRUE ~ NA))
    
    },
    .id = "Var"
  )%>% 
  separate(Var, into = c("Var", "area_id"), sep = "X") %>% 
  mutate(area_id = factor(area_id, levels = areas_northsouth))

Temperature Suitability Shifts

The results can be seen below:

Code 
# Summarise the breakpoint locations
temp_suit_shift_points <- temp_range_rstars %>% 
  filter(RSI != 0) %>% 
  dplyr::select(time, Var, area_id, shift_direction)


# Plot the breaks over the monthly data
ggplot() +
   geom_vline(
    data = temp_suit_shift_points,
    aes(
      xintercept = time,
      color = shift_direction),
    linewidth = 1.5) +
  geom_line(
    data = temp_range_rstars,
     aes(time, Value),
    linewidth = 0.4, alpha = 0.5) +
  scale_color_gmri() +
  facet_grid(area_id ~ Var, labeller = label_wrap_gen(width = 8), scales = "free") +
  scale_x_date(
    breaks = seq.Date(
      from = as.Date("1950-01-01"),
      to = as.Date("2020-01-01") ,
      by = "10 year"),
     limits = as.Date(c("1970-01-01", "2020-01-01")),
    labels = scales::label_date_short()) +
  theme(
    legend.position = "bottom",
    strip.text.y = element_text(angle = 0)) + 
  labs(color = "",
       y = "Measurement",
       x = "Date",
       title = "Lobster Thermal Preferences - Detrended",
       subtitle = "STARS Regime Shifts")

Based on the annual totals, we see limited breakpoints in suitable thermal habitat.

Code 
temp_suit_shift_points %>% 
  group_by(Var) %>% 
  arrange(time, area_id) %>% 
  gt::gt() %>% 
  gt::tab_header(title = "Temperature Suitability Scale Breaks")
Temperature Suitability Scale Breaks
time area_id shift_direction
Preferred Bottom Temperatures 10-18C
1985-01-01 New Jersey Shore Shift Down
1985-01-01 Barnegat Ridge Shift Down
2008-01-01 Barnegat Ridge Shift Up
Heat Stress Conditions >18C
1992-01-01 Virginia Shore Shift Down
2004-01-01 Rhode Island Shore Shift Down
2011-01-01 Five Fathom Bank Shift Up
2012-01-01 Rhode Island Shore Shift Up

And restricted to these areas

Code 
ggplot() +
  geom_sf(data = filter(
    study_regions, 
    SHORT_NAME %in% temp_suit_shift_points$area_id),
    fill = gmri_cols("lv orange"), alpha = 0.4) +
  geom_sf(data = new_england) +
  geom_sf(data = canada) +
  
  coord_sf(xlim = c(-78, -66), ylim = c(35.5, 45)) +
  labs(title = "Affected Areas")

Summary Figures